一种基于深度学习的定量计算机断层扫描模型用于预测新型冠状病毒肺炎的严重程度：一项对196例患者的回顾性研究
A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients.
作者信息
Shi Weiya, Peng Xueqing, Liu Tiefu, Cheng Zenghui, Lu Hongzhou, Yang Shuyi, Zhang Jiulong, Wang Mei, Gao Yaozong, Shi Yuxin, Zhang Zhiyong, Shan Fei
机构信息
Department of Radiology, Shanghai Public Health Clinical Center, Fudan University, Shanghai, China.
Institutes of Biomedical Sciences, Fudan University, Shanghai, China.
出版信息
Ann Transl Med. 2021 Feb;9(3):216. doi: 10.21037/atm-20-2464.
BACKGROUND
The assessment of the severity of coronavirus disease 2019 (COVID-19) by clinical presentation has not met the urgent clinical need so far. We aimed to establish a deep learning (DL) model based on quantitative computed tomography (CT) and initial clinical features to predict the severity of COVID-19.
METHODS
One hundred ninety-six hospitalized patients with confirmed COVID-19 were enrolled from January 20 to February 10, 2020 in our centre, and were divided into severe and non-severe groups. The clinico-radiological data on admission were retrospectively collected and compared between the two groups. The optimal clinico-radiological features were determined based on least absolute shrinkage and selection operator (LASSO) logistic regression analysis, and a predictive nomogram model was established by five-fold cross-validation. Receiver operating characteristic (ROC) analyses were conducted, and the areas under the receiver operating characteristic curve (AUCs) of the nomogram model, quantitative CT parameters that were significant in univariate analysis, and pneumonia severity index (PSI) were compared.
RESULTS
In comparison with the non-severe group (151 patients), the severe group (45 patients) had a higher PSI (P<0.001). DL-based quantitative CT indicated that the mass of infection (MOI) and the percentage of infection (POI) in the whole lung were higher in the severe group (both P<0.001). The nomogram model was based on MOI and clinical features, including age, cluster of differentiation 4 (CD4) T cell count, serum lactate dehydrogenase (LDH), and C-reactive protein (CRP). The AUC values of the model, MOI, POI, and PSI scores were 0.900, 0.813, 0.805, and 0.751, respectively. The nomogram model performed significantly better than the other three parameters in predicting severity (P=0.003, P=0.001, and P<0.001, respectively).
CONCLUSIONS
Although quantitative CT parameters and the PSI can well predict the severity of COVID-19, the DL-based quantitative CT model is more efficient.
背景
目前，通过临床表现评估2019冠状病毒病（COVID-19）的严重程度尚未满足紧迫的临床需求。我们旨在建立一种基于定量计算机断层扫描（CT）和初始临床特征的深度学习（DL）模型，以预测COVID-19的严重程度。
方法
2020年1月20日至2月10日，我们中心纳入了196例确诊为COVID-19的住院患者，并将其分为重症组和非重症组。回顾性收集两组患者入院时的临床放射学数据并进行比较。基于最小绝对收缩和选择算子（LASSO）逻辑回归分析确定最佳临床放射学特征，并通过五折交叉验证建立预测列线图模型。进行受试者操作特征（ROC）分析，并比较列线图模型、单因素分析中有意义的定量CT参数和肺炎严重程度指数（PSI）的受试者操作特征曲线下面积（AUC）。
结果
与非重症组（151例患者）相比，重症组（45例患者）的PSI更高（P<0.001）。基于DL的定量CT显示，重症组全肺感染质量（MOI）和感染百分比（POI）更高（均P<0.001）。列线图模型基于MOI和临床特征，包括年龄⁃分化簇4（CD4）T细胞计数、血清乳酸脱氢酶（LDH）和C反应蛋白（CRP）。该模型、MOI、POI和PSI评分的AUC值分别为0.900、0.813、0.805和0.751。在预测严重程度方面，列线图模型的表现明显优于其他三个参数（P分别为0.003、0.001和P<0.001）。
结论
虽然定量CT参数和PSI可以很好地预测COVID-19的严重程度，但基于DL的定量CT模型效率更高。
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